Quality Analysis method and program

ABSTRACT

A graphical user interface for a quality analysis computer program includes a first display area for displaying graphical representations of statistical data and a second display area containing a tree representation of a quality analysis project. The tree representation may include one or more quality analysis project steps, one or more statistical tool categories associated with the one or more quality analysis project steps, and one or more statistical tools associated with the one or more statistical tool categories.

FIELD OF THE INVENTION

The present invention relates generally to the field of quality analysistools and programs.

BACKGROUND OF THE INVENTION

Heightened consumer demand for highly reliable goods and services hasmade quality an increasingly important issue for businesses. Thisever-growing consumer demand for quality has lead to an increasedemphasis placed on quality control and improvement at virtually alllevels of business operations, including engineering, manufacturing,distribution, and other administrative operations. Over the years,various quality analysis tools and computer programs have been developedin an attempt to aid businesses analyze and improve the quality of theirprocesses and products. Such conventional tools and computer programs,however, suffer from a number of drawbacks and deficiencies.

For example, while many conventional quality analysis computer programsare capable of performing various statistical tasks and experimentsthat, when performed correctly, may help businesses identify ways toimprove the quality of their products and processes, the user interfaceemployed in such programs is typically designed for use by highlyskilled statisticians. In particular, the terminology used, the userinput required to design tests and experiments, and the manner in whichthe results of such experiments are displayed in conventional qualityanalysis programs typically require specialized training and expertisein the field of statistics for sufficient operation and comprehension ofthe same. As a result, business owners, management personnel, and otherdecision makers having minimal experience in statistics generally findthe use and application of such programs intimidating, problematic, andunproductive.

Accordingly, there exists a need for a system, method and programcapable of maximizing the functionality and success of processimprovement projects, while minimizing the effort and complexitypresented to the user. More particularly, there exists a need for asimplified user interface for a quality analysis computer programcapable of enabling persons with only minimal training in statistics tosimply, efficiently and effectively analyze and improve processes.

SUMMARY OF THE INVENTION

In certain embodiments, a graphical user interface for a qualityanalysis computer program, which may be stored as computer-readableinstructions on a computer-readable medium, includes a first displayarea for displaying graphical representations of statistical data and asecond display area containing a tree representation of a qualityanalysis project. The tree representation may include one or morequality analysis project steps, one or more statistical tool categoriesassociated with the one or more quality analysis project steps, and oneor more statistical tools associated with the one or more statisticaltool categories. One or more status indicators configured to graphicallydisplay the status of each quality analysis project step in the qualityanalysis project may also be displayed in the second display area.

In certain embodiments, the graphical representations comprise a “gasgauge” type graph for graphically representing the results of astatistical test. These graphical representations may also comprise oneor more slider bars for graphically representing the results of astatistical test.

The interface may also comprise a capability study display areadisplayed in the first display area and configured to display theresults of a capability study. In many embodiments, the capability studydisplay area comprises a first portion displaying a percentage ofnon-conforming material produced in a process, a second portiondisplaying a percentage of non-conforming material that would beproduced in the process if an extraordinary variation is removed, athird portion displaying a percentage of non-conforming material thatwould be produced in the process if the mean of the process is centeredbetween the specification limits, and a fourth portion displaying apercentage of non-conforming material that would be produced in theprocess if the extraordinary variation is removed and the mean of theprocess is centered.

In certain aspects, the graphical representations may also comprise atrended process behavior chart comprising a trendline serving as acenterline of the process behavior chart and parallel limit lines havingthe same slope as and positioned to surround the trendline. Theinterface may also comprise a behavior chart interface displayed in thefirst display area and configured to enable a user to split a processbehavior chart into segments, with the behavior chart comprising aninput box for receiving a beginning point of each segment of the splitprocess behavior chart.

According to at least one embodiment, the graphical representationscomprise a chart graphically representing statistical data and a datatable containing the statistical data represented in the chart; whereinstatistical data selected by a user in the chart is automaticallyhighlighted in the data table. Statistical data selected by a user inone of the graphical representations may also be automaticallyhighlighted in other graphical representations.

The graphical representations may also comprise a process map configuredto display the order of one or more process steps; wherein the order ofthe process steps is modifiable by a user. A step efficiency value mayalso be displayed in each process step. The interface may also furthercomprise one or more test variables for use in the quality analysisproject steps and the statistical tools of the quality analysis project;wherein the statistical data and test variables used in prior qualityanalysis project steps and statistical tools are automatically carriedforward for use in subsequent quality analysis project steps andstatistical tools.

In certain embodiments, the graphical representations comprise a chartillustrating the values of one or more variables ranked in order oftheir potential influence on a process, with one or more of thevariables configured to be removable by a user. The interface may alsofurther comprise one or more user-defined input variables for use inperforming the quality analysis project steps, with one or more of theuser-defined variables designated by a user as an output variable, aninterval input variable, or a categorical input variable.

In at least one embodiment, a regression method is used to determine therelationship between the user-defined variables when one or moreuser-defined variables are designated as interval input variables. Onthe other hand, and an analysis of variance (ANOVA) method may be usedto determine the relationship between the user-defined variables whenone or more of the user-defined variables are designated as categoricalinput variables.

In many embodiments, the graphical representations comprise a graphicalgauge for graphically representing a power value of a statistical test.This graphical gauge may be a “gas gauge” type graph or a slider bar.The interface may also further comprise a factorial experiment designarea displayed in the first display area, the factorial experimentdesign area comprising a first portion for allowing a user to select anexperiment type, a second portion for allowing the user to select afactor number and a run number, a third portion for allowing the user todesignate an acceptable alpha risk, and a fourth portion for allowingthe user to designate a replicate value, a centerpoint value, and ablock value. The factorial experiment design area may also furthercomprise a fifth portion for allowing the user to designate aninteraction value and a sixth portion for allowing the user to designatea P limit value.

According to at least one embodiment, the graphical representationscomprise a scree plot. The graphical representations may also comprise afractional factorial display configured to graphically represent theresults of a fractional factorial experiment; wherein statisticallyinsignificant results of the fractional factorial experiment areautomatically highlighted in the fractional factorial display. Inaddition, the graphical representations may comprise a first graphillustrating the distribution of a T-test when the actual differencebetween two population samples is assumed to be less than apre-determined number, a second graph illustrating the distribution of aT-test when the actual difference between the two population samples isassumed to be more than the pre-determined number, and a third graphillustrating the distribution of a T-test when the actual differencebetween the two population samples is assumed to be different from thepre-determined number.

The graphical representations may also comprise one or more data boxescontaining statistical data from a statistical test, and explanatorystatement boxes containing text explaining the significance of thestatistical data contained in the data boxes. Still further, thegraphical representations may comprise a first process behavior chartfor graphically illustrating the value of variables in a single inputvariable process, and a second process behavior chart for graphicallyillustrating the value of residuals in a multiple input variableprocess. The graphical representations may also comprise a first inputbox for receiving a sample size value, a second input box for receivinga difference value, and a third input box for receiving a power value;wherein the sample size value, the difference value, or the power valueof the test is automatically computed and displayed after the other twovalues are entered by a user.

In at least one embodiment, the graphical representations comprise aprocess behavior chart generated for a user-selected input variable. Thegraphical representations may also comprise a capability processbehavior chart and a capability histogram simultaneously displayed withthe process behavior chart. In addition, the graphical representationsmay comprise a chart comprising a user-defined upper and lowerspecification limits illustrating the upper and lower specificationlimits of a process and upper and lower uncertainty zones configured tographically display the amount of uncertainty present in a measurementsystem relative to the width of the upper and lower specificationlimits.

According to certain embodiments, the graphical representations comprisea final project report, the final project report comprising one or moregraphical summaries of each step in the quality analysis project;wherein the final project report is automatically generated uponcompletion of each in the quality analysis project. The graphicalrepresentations may also comprise a single process behavior chartarranged into subgroups; wherein the centerline of each subgroup of theprocess behavior chart is centered on the subgroup mean.

The graphical representations may also comprise a chart comprising oneor more main variable columns graphically representing the magnitude ofan effect caused by a main input variable, one or more secondaryvariable columns blocked within each main variable column andgraphically representing the magnitude of an effect caused by asecondary input variable, and a line graph displayed within each mainvariable column and graphically representing the cumulative sum of thevalues of each secondary variable column.

In at least one embodiment, a system for displaying graphicalrepresentations of statistical data comprises a computer, a qualityanalysis program having a graphical user interface and capable of beingexecuted by the computer, and a display device controlled by thecomputer and configured to display the graphical user interface of thequality analysis computer program; wherein the graphical user interfaceof the quality analysis computer program comprises a first display areafor displaying graphical representations of statistical data, and asecond display area containing a tree representation of a qualityanalysis project, the tree representation. The tree representation maycomprise one or more quality analysis project steps, one or morestatistical tool categories associated with the one or more qualityanalysis project steps, and one or more statistical tools associatedwith the one or more statistical tool categories.

In certain embodiments, a method for displaying graphicalrepresentations of statistical data comprises providing a first displayarea for displaying graphical representations of statistical data, andproviding a second display area for displaying a tree representation ofa quality analysis program. In many embodiments, the tree representationcomprises one or more quality analysis project steps, one or morestatistical tool categories associated with the one or more qualityanalysis project steps, and one or more statistical tools associatedwith the one or more statistical tool categories.

Features from any of the above-mentioned embodiments may be used incombination with one another in accordance with the present invention.These and other embodiments, features and advantages will be more fullyunderstood upon reading the following detailed description inconjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate exemplary embodiments of thepresent invention and are a part of the specification. Together with thefollowing description, the drawings demonstrate and explain theprinciples of the present invention.

FIG. 1 is a block diagram of the components of an exemplary computersystem capable of generating a graphical user interface for a qualityanalysis computer program.

FIG. 2A is an illustration of a main display area and a sidebar displayarea of a graphical user interface for a quality analysis programaccording to one exemplary embodiment.

FIG. 2B is a magnified view of the sidebar display area illustrated inFIG. 2A.

FIG. 3A is an illustration of an exemplary “gas gauge” type graph forgraphically representing statistical data according to one embodiment.

FIG. 3B is an illustration of an exemplary “slider bar” type graph forgraphically representing statistical data according to an alternativeembodiment.

FIG. 4 is an illustration of an exemplary manner for graphicallypresenting the results of a capability study.

FIG. 5 is an illustration of an exemplary process behavior chartconfigured to graphically represent trended data.

FIG. 6A is an illustration of an exemplary graphical interface forsplitting a process behavior chart.

FIG. 6B is an illustration of an exemplary process behavior chart splitin accordance with the values input by a user in the interfaceillustrated in FIG. 6A.

FIG. 7A is an illustration of an exemplary process behavior chart havingone data point graphically selected by a user.

FIG. 7B is an illustration of a data table having a highlighted datapoint value corresponding to the data point selected in FIG. 7B.

FIG. 8 is an illustration of an exemplary manner for graphicallyhighlighting a data point selected by a user in a first display in otherdisplays.

FIG. 9 is an illustration of an exemplary graphical user interface for adynamic process map.

FIG. 10 is an illustration of a step efficiency value incorporated intoa process map.

FIG. 11 is an illustration of an exemplary cause and effect Paretodiagram.

FIG. 12 is an illustration of an exemplary graphical user interface fora data table.

FIG. 13 is an illustration of an exemplary graphical user interface forfactorial experiment design.

FIG. 14 is an illustration of an exemplary graphical user interface forthe automatic reduction of insignificant variables in an experiment.

FIG. 15 is an illustration of an exemplary scree plot.

FIG. 16A is an illustration of an exemplary manner of displaying theresults of a fractional factorial experiment.

FIG. 16B is an illustration of the results of the fractional factorialexperiment in FIG. 16A, with the signs of each input variable reversed.

FIG. 16C is an illustration of an alternative exemplary graphicalrepresentation of the results of a fractional factorial experiment.

FIG. 17A is an illustration of an exemplary graphical representation ofthe results of a T-test.

FIG. 17B is an illustration of an alternative exemplary graphicalrepresentation of the results of a T-test.

FIG. 18 is an illustration of an exemplary graphical representation ofthe results of one or more tests for homogeneity.

FIG. 19 is an illustration of an exemplary graphical user interface forenabling a user to determine an appropriate power value, differencevalue, and sample size for a statistical test.

FIG. 20A is an illustration of an exemplary graphical user interface forsorting the results of a test based on an input variable.

FIG. 20B is an illustration of an exemplary process behavior chartgenerated based on an input variable selected in FIG. 20A.

FIG. 20C is an illustration of an exemplary process behavior chartgenerated based on an additional input variable selected in FIG. 20A.

FIG. 21 is an illustration of an exemplary manner of graphicallyrepresenting the amount of uncertainty present in a test.

FIG. 22 is an illustration of an exemplary process behavior chartarranged into subgroups.

FIG. 23 is an illustration of a modified Pareto chart according to oneembodiment.

FIG. 24 is an illustration of an exemplary final report according to oneembodiment.

FIG. 25 is a chart illustrating the flow of variables between variousproject steps in a quality analysis computer program according to oneembodiment of the present invention.

Throughout the drawings, identical reference characters and descriptionsindicate similar, but not necessarily identical, elements. While thepresent invention is susceptible to various modifications andalternative forms, specific embodiments have been shown by way ofexample in the drawings and will be described in detail herein. However,one of skill in the art will understand that the present invention isnot intended to be limited to the particular forms disclosed. Rather,the invention covers all modifications, equivalents and alternativesfalling within the scope of the invention as defined by the appendedclaims.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

FIG. 1 is a block diagram of the components of an exemplary computersystem 100 capable of generating a graphical user interface for aquality analysis computer program. Generally speaking, computer system100 comprises one or more processors 104 connected to a communicationsinfrastructure 102. As will be appreciated by those of skill in the art,computer system 100 generally represents any single or multi-processorcomputer or computer system capable of executing single-threaded ormulti-threaded applications. Communications infrastructure 102 generallyrepresents any form of structure capable of facilitating communicationbetween one or more electronic components; including, for example, acommunication bus, a cross-bar, or a network.

In at least one embodiment, computer system 100 further comprises a mainmemory 106 and a secondary memory 108 operably connected to processor104 via communications infrastructure 102. Main member 106 and secondarymemory 108 generally represent any form of storage device or mediumcapable of storing data and other computer-readable instructions. Incertain embodiments, main memory 106 is a random access memory (RAM)unit. Secondary memory 108 may also comprise one or more additionalstorage devices or mediums, including, for example, a hard disk drive110 and/or a removable storage drive 112.

Removable storage drive 112 generally represents any form of removablestorage device capable of communicating with processor 104 viacommunications infrastructure 102. For example, removable storage drive112 may be a floppy disk drive, a magnetic tape drive, an optical diskdrive, a flash drive, or the like. In certain embodiments, removablestorage drive 112 is configured to read from and/or write to a removablestorage unit 114 in a manner well known to those of skill in the art.Generally speaking, removable storage unit 114 represents any form ofstorage device or medium capable of being written to and/or read byremovable storage drive 112; including, for example, a floppy disk, amagnetic tape, an optical disk, a flash memory device, or the like. Aswill be appreciated by those of skill in the art, in at least oneembodiment removable storage unit 114 is configured to store thereincomputer software, data or other computer-readable information.

Secondary memory 108 may also include other similar structures forallowing computer software, data or other computer-readable instructionsto be loaded into computer system 100. Such structures can include, forexample, a removable storage unit 118 and an interface 116. Examples ofsuitable configurations of removable storage unit 118 and interface 116include a program cartridge and cartridge interface, a removable memorychip (such as a flash memory chip, an EEPROM, EPROM, PROM, FRAM, MRAM,or other similar non-volatile memory chip) and associated socket, andother removable storage units 118 and interfaces 116 configured to allowsoftware and data to be transferred from the removable storage unit 118to computer system 100.

In many embodiments, computer system 100 also includes a displayinterface 120 that forwards graphics, text, and other data from thecommunications infrastructure 102 (or from a frame buffer, not shown)for display on a display device 122. Generally speaking, display device122 represents any form of device capable of visually displayinginformation forwarded by display interface 120. Examples of displaydevice 122 include CRT monitors, LCD screens, plasma screens, videoprojectors, and the like.

According to certain embodiments, one or more computer programs (alsoknown as computer software, computer-readable instructions, or computercontrol logic) may be stored in main memory 106 and/or various portionsof secondary memory 108. In addition, although not illustrated in FIG.1, computer programs may also be loaded into computer system 100 via acommunications interface (such as a modem, network interface,communications port, or the like) connected to various external devices.Such computer programs, when executed, enable computer system 100, andmore particularly processor 104, to perform the features of the presentinvention as discussed herein.

As will be appreciated by those of skill in the art, a quality analysiscomputer program and graphical user interface for the same (discussed ingreater detail below) can be implemented as control logic in software,firmware, hardware or any combination thereof. When implemented usingsoftware, the software may be stored in a computer-readable medium andloaded into computer system 100 using hard drive 110, removable storagedrive 112 or interface 116, or downloaded to computer system 100 over acommunications path, such as over the Internet or other network. As usedherein, the phrase “computer-readable medium” generally refers to astorage device or medium, such as removable storage units 112, 114 or ahard disk stored in hard disk drive 110, capable of storing a computerprogram. As detailed above, examples of such media include magneticmedia, optical media, flash media, or other recordable media, or mediathat transmits a carrier wave or other signal. Generally speaking, thesecomputer-readable media represent a manner of providing a computerprogram (such as a quality analysis computer program) to computer system100. When executed by processor 104, a computer program loaded intocomputer system 100 causes processor 104 to perform the functions of theinvention as described herein.

Alternatively or in combination therewith, a quality analysis computerprogram and graphical user interface for the same may be implemented infirmware and/or hardware using, for example, hardware components such asapplication specific integrated circuits (ASICs). Implementation of ahardware state machine for performing the functions described hereinwill be apparent to persons skilled in the art.

In at least one embodiment, a graphical user interface for a qualityanalysis computer program is displayed on display device 122 to providethe user with a convenient, visual mechanism for controlling theoperation of computer system 100 and the computer program executedtherein. As will be appreciated by those of skill in the art, thisquality analysis computer program and graphical user interface for thesame may be configured to operate on any type of platform and on anyvariety of operating system. Generally speaking, to provide input andoutput functionality, a graphical user interface according to thepresent invention may include various types of menus and control objectswhich enable a user thereof to select from available choices. Examplesof such control objects include push buttons, via which the user canindicate acceptance of a particular action, radio buttons for selectingone of a number of available choices for a particular parameter, andcheck boxes for activating or deactivating various features. Otherexamples of such control objects include scroll bars for displayingdifferent portions of a document within a window, sliders for adjustingvariable values, and disclosure triangles for displaying or hiding thecontents of a folder or a pop-up menu.

In many embodiments, a user may activate each of these various objectsby positioning a cursor on it using a user input device (such as amouse) connected to computer system 100, and actuating the object, bypushing a button or the like on the user input device. Selection of menuitems may occur in a similar manner. For example, the user may positionthe cursor over the desired command displayed on the menu, and thenpress or release a control button on the user input device. When thisoccurs, the operating system of computer system 100 detects the positionof the cursor, and if it is located over a menu command or a controlobject, executes the function associated with the selected command.

As will be appreciated by those of skill in the art, each of these userinterface objects has two main properties associated with it; namely,its appearance and its functionality. In general, the functionality ofan object relates to the command that is executed by the computer inresponse to actuating the object. For example, actuating a push buttonthat is labeled “OK” may cause computer system 100 to execute aparticular action, such as creating a graphic or chart for display ondisplay device 122. The functionality associated with an object may alsoinclude a appearance characteristic in which the object occupiesdifferent states in dependence upon user actions. For example, when apush button is actuated or a menu command is selected, the appearance ofthe object itself, or other objects associated therewith, may changefrom a normal state to a highlighted state.

As detailed above, in at least one embodiment of the present invention aquality analysis program having a graphical user interface is loadedinto computer system 100. A more detailed explanation of variousexemplary features of this quality analysis program and its graphicaluser interface will now be provided with reference to the relevantfigures.

As seen in FIGS. 2A and 2B, a graphical user interface for a qualityanalysis program according to certain embodiments of the presentinvention comprises a display area 200 divided into a main display area210 and a sidebar display area 220. Generally speaking, any number orcombination of textual or graphical displays or interfaces may bedisplayed within main display area 210; including, for example,diagrams, charts, plots, tables, lists, graphs, interfaces for designingstatistical experiments, and the like. In at least one embodiment, maindisplay area 210 is configured to display one or more graphicalrepresentations of statistical data generated by a quality analysisprogram. For example, main display area 210 may be used to display oneor more of the graphical representations of statistical data illustratedin FIGS. 3-23, each of which will be discussed in greater detail below.

In at least one embodiment, sidebar 220 is designed to display thestructure and/or progress of a quality analysis project comprising oneor more quality analysis project steps 222. Generally speaking, thenumber and nomenclature of project steps 222 may be chosen to mirror theproject steps used by any one of various quality analysis or improvementtools or programs; including, for example, Six Sigma, Lean,Plan-Do-Check-Act, the Toyota Production System, Total Quality analysis(TQM), and other such tools and programs. In the exemplary embodimentprovided in FIGS. 2A-2B, project steps 222 mirror the project stepscommonly used in a Six Sigma roadmap; namely, Define, Measure, Analyze,Improve, and Control.

As seen in FIGS. 2A and 2B, sidebar 220 may be designed to display thehierarchical structure and/or progress of a quality analysis project inwhat is known as a tree view or representation. Generally speaking, thetree view displayed in sidebar 220 visually indicates the level in thehierarchy where each item resides. In at least one embodiment, the treeview displayed in sidebar 220 is comprised of one or more project steps222 representing the highest or “root” level of the tree representation,one or more tool categories 224 representing one or more “branches” ofeach project step 222, and one or more tools 226 representing one ormore “branches” of each tool category 224. As will be appreciated bythose of skill in the art, a “branch” refers to an entry in a treerepresentation at a particular level. Conventionally, items that are onelevel in the tree representation below a first object are referred to asthe “children” of the first object. Thus, for example, in FIG. 2A thetool category 224 labeled “Prioritize” is a child of the project step222 labeled “Analyze.” In many embodiments, a user may “expand” or“collapse” the tree at various points (i.e., display or hide informationin the lower levels) to facilitate viewing of the various hierarchicalitems.

As seen in FIGS. 2A and 2B, a status indicator 228 may be positionednext to each item in sidebar 220 to indicate the status of each step andsub-step in the quality analysis project. Although status indicators 228may be of any size or shape, in at least one embodiment statusindicators 228 are circular in shape. Generally speaking, statusindicators 228 graphically indicate the status of each step and sub-stepin the quality analysis project through the use of one or more graphicalicons. For example, as seen in FIGS. 2A-2B, when a step or sub-step inthe quality analysis project has been completed, a graphic, such as acheck mark, may be displayed inside the status indicator 228 positionednext to the completed step or sub-step to indicate that this step orsub-step has been completed. Similarly, a graphic, such as a dot, may bedisplayed inside status indicator 228 to indicate that a step has beenstarted, but not yet completed (i.e., that the step or sub-step is “InProgress”). Likewise, circular status indicators 228 may be left emptyor blank to indicate that a step or sub-step has not yet begun. Whileone or more of steps 222, categories 224, or tools 226 may be displayedin sidebar 220 at any point, in at least one embodiment only those tools226 necessary for completion of a project step 222 are displayed. Forexample, the tool 226 labeled “Capability Study” may only be displayedwithin sidebar 220 when the project step 222 labeled “Measure” is “InProgress.”

According to additional embodiments, when desired, further detailedinformation about each step or sub-step or the project as a whole may bedisplayed in main display area 210. For example, a user desiring furtherdetailed information may select a specific step or sub-step using aninput device, such as a mouse, to request the display in main display210 of a detailed report on the selected step or sub-set. Examples ofthe type of information that may be displayed in this report include,without limitation, information regarding the persons involved in eachstep, the date and time of completion of each step, the type of savingsexpected to be generated by each step, the total estimated savings to begained through the project, grouped by category, and the estimated dateson which these savings will commence.

The simplified tree view and status indicators 228 in sidebar 220 thusenable a user to quickly and easily determine: (1) the number of stepsand sub-steps in a quality analysis project; (2) the organization ofeach step and sub-step in the project; (3) which steps and sub-steps inthe project have been completed; (4) which steps are in the process ofbeing completed; and (5) how many steps remain to be completed. Further,a detailed report on each step, sub-step, or the project as a whole maybe easily and quickly requested by a user when desired. Sidebar 220 maythus serve to simplify the task of a manager or supervisor in charge ofthe quality analysis project and assist project leaders in staying ontask. Although sidebar 220 has been described and illustrated as asidebar positioned to the side of main display 210, other configurationsare within the scope of this invention. For example, sidebar 220 may bepositioned above, below, next to, or within main display 210.

As detailed above, conventional quality analysis tools and programstypically display the output of various statistical tests andexperiments as series of data in table format. For example, thefollowing table represents a conventional manner for displaying theresults of an exemplary statistical test known as an Analysis ofVariance test (also referred to as an ANOVA test): TABLE 1 Seq Seq AdjAdj MS Source DF SS Var SS Var Adj F VIF P Material 4 32431 82.11% 3243182.11% 8108 154.9 1 0 batch Model 4 32431 82.11% 32431 82.11% 8108 154.90 Error 135 7067  17389%  7067  17389%  52.35 Total 139 39498   100%39498   100%R-Sq = 82.11%;R-Sq Adj = 81.58%;Epsilon-Sq = 82.11%Tests of Equal Variance:Levene = 1.529,P = 0.2;Bartlett (Normal) = 7.068,P = 0.13Y = 58.26 + [data1: 0.7675 data2: 25.7 data3: −9.448 data 4: 2.957data5: −19.98]

Although a skilled statistician may be able to correctly interpret theresults of the exemplary ANOVA test presented in Table 1 with marginaleffort, others with only minimal statistical training or experience maystruggle to quickly and effectively interpret these results.Accordingly, in at least one embodiment, the results of statisticaltests may be visually displayed in main display area 210 in graphicalformats that are both easily and quickly interpretable by users havingeven minimal statistical training or experience.

For example, FIGS. 3A and 3B are illustrations of graphicalrepresentations of statistical data that may be displayed in maindisplay 210. As seen in FIG. 3A, in certain embodiments a “gas gauge”type graph 250 may be used to graphically represent the results of astatistical test or experiment. In at least one embodiment, gas gaugegraph 250 comprises a first scale 252 and a second scale 254, a pointer256 and an indicator 258. Generally speaking, scales 252 and 254 may beused to represent the range of possible values for one or more outputvariables of various statistical experiments or tests, while pointer 256and indicator 258 indicate the general values of these output variables.For example, in certain embodiments scales 252, 254 may be used torepresent the range of values possible for the outputs P (probabilityvalue) and R² (coefficient of determination), respectively, of an ANOVAtest. In this exemplary embodiment, the values along first scale 252 runfrom of 0 to 1.00 (from right to left) to represent the range ofpossible values for the output P. Similarly, the values along secondscale 254 run from 0% to 100% (from left to right) to represent therange of possible values for the output R².

In certain embodiments, various portions of first scale 252 may be colorcoded to indicate general ranges of the scale. For example, a portion Rnearest the right end of first scale 252 may be colored red, a portion Onext to this portion may be colored orange, a portion Y next to thisportion may be colored yellow, with the remaining portion G coloredgreen. In at least one embodiment, the junction between portion G andportion Y represents a P value of around 0.10, while the junctionbetween portion O and portion R represents a P value of around 0.05. Asknown to those of skill in the art, the P value of a statistical test isthe probability of getting a value of the test statistic as extreme asor more extreme than that observed when there is no effect in the testedpopulation. In other words, the P value indicates the probability ofgetting something more extreme than the test's result by chance alone.Conventionally, P values of 0.05 or less are generally interpreted asindicating that the difference caused by the tested variable is real(i.e., not attributable to chance). Accordingly, by color coding variousportions of first scale 252, a user may quickly determine whether thedifference caused by the variable being studied is real or merelyattributable to chance. In particular, so long as pointer 258 is notpositioned within or near red portion R, a user may quickly and reliablyassume that the difference caused by the tested variable is likelyattributable to chance.

The coefficient of determination (commonly designated R²), on the otherhard, refers to a percentage of variation in the dependent variable thatis “explained” by variation in the independent variable(s). R² is, thus,a measure of “explanatory power.” As such, indicator 258 positionedalong second scale 254 serves to indicate the general percentage ofvariation accounted for by the tested variable. By graphicallyrepresenting the value of R² in this simplified manner, second scale 254and indicator 258 thus enable a user to quickly and intuitivelyinterpret the R² results of a statistical test or experiment.

Accordingly, by providing a simple and intuitive graphicalrepresentation of the output of a statistical test, gas gauge 250simplifies the interpretation of the results of statistical tests andshortens the amount of time and effort conventionally required toproperly interpret these results. Exemplary gas gauge 250 thus enablespersons of even minimal skill in the art of statistics to interpret andapply the results of various statistical tests.

Although gas gauge 250 has been described as being used to graphicallydisplay the output values of P and R² from an ANOVA test, persons ofskill in the art will appreciate that gauge 250 may also be configuredto graphically represent the values or results of any number of otherstatistical tests or experiments. For example, gas gauge 250 may beconfigured to graphically represent the power value of a statisticaltest (as discussed in greater detail below), the results of an equalityof variances test, the results of a normality of residuals test, or thelike. As is known to those in the art, a “residual” is the value from anoriginal data set after the effects of the variables in this set havebeen removed.

FIG. 3B is an illustration of exemplary “slider bar” type graphs 260 forgraphically representing statistical data according to an alternativeembodiment. In the exemplary embodiment illustrated in this figure,slider bar graphs 260 generally comprise a slider scale 262 and a sliderbar 264. Similar to scales 252, 254 in FIG. 3A, slider scale 262represents the range of possible values for one or more output variablesof various statistical experiments or tests, while slider bar 264 isused to indicate the actual value of these output variables. Althoughthe range of slider scale 262 may encompass any number of values, in atleast one embodiment the values along scale 262 run from 100% to 0%(from top to bottom) to indicate a percentage value.

While slider bar graphs 260 may be used to graphically represent thevalues or results of any number of statistical tests or experiments, inthe exemplary embodiments illustrated in FIG. 3B slider bar graphs 260are used to graphically represent the results of an equality ofvariances test and a normality of residuals test. As is well known tothose of skill in the art, the equality of variances test and thenormality of residuals tests are conventionally performed to confirm theaccuracy of assumptions relied upon in various statistical tests. Asseen in this figure, slider bar graphs 260 may visually display thepercentage of variances that are equal (as determined by an equality ofvariances test, such as the Levene or BartletT-test) or the percentageof residuals deemed “normal” (as determined by a normality of residualstest) by positioning slider bar 264 along slider scale 262.

According to certain embodiments, similar to first slider scale 252 inFIG. 3A, slider scale 262 is divided into one or more color codedregions, such as a green region G, a yellow region Y, an orange regionO, and a red region R. In at least one embodiment, the percentage valuescovered by each region are chosen so as to appropriately indicate therelative likelihood that the assumptions tested by the equality of thevariances test and the normality of residuals test have beensuccessfully checked and met. For example, as seen in FIG. 3B, the sizeof green region G may be chosen so as to cover all percentage valueswithin which an assumption may be deemed to have been satisfied (suchas, for example, 100% to 10%). Similarly, the size of red region R maybe chosen to encompass all percentage values within which an assumptionmay be deemed to not have been satisfied (such as, for example, allpercentage values less than 5%). These color coded regions help a userto quickly and efficiently identify when the statistical assumptionsrelied upon have been checked and verified as valid. In particular, inthe example illustrated in FIG. 3B, a user need only verify that eachslider bar 262 falls within green region G, which indicates that theassumptions have been checked and verified as valid.

Although gas gauge 250 and slider bars 260 have been provided asexamples of the types of graphical representations that might be used tovisually display and represent the results of various statistical testsand experiments, it will be appreciated that various other graphicalrepresentations for displaying these results fall within the scope ofthe present invention. In particular, persons of skill in the art willappreciate that graphical representations 250, 260 may be modified asneeded to correctly and simply display the results of any variety ofstatistical test or experiment.

FIG. 4 is an illustration of an additional graphical representation ofstatistical data that may be displayed in main display area 210. Morespecifically, FIG. 4 illustrates an exemplary manner for graphicallypresenting the results of a capability study. As is known to those ofskill in the art, four capability indices (namely, Process Capability(Cp), Process Capability Index (Cpk), Process Performance (Pp), andProcess Performance Index (Ppk)) are conventionally used in a capabilitystudy to determine the quality and capability of a process.Advantageously, these capability indices may also be used to diagnoseproblems in a process and to predict the number of defective parts thatwill be produced by this process. In the hands of a skilledstatistician, the combined use of these capability indices in acapability study may assist a user in devising a strategy for improvingthe quality of a process. Considerable skill is, however, typicallyrequired to extract a useful process improvement strategy from thesefour conventional capability indices.

In the exemplary embodiment illustrated in FIG. 4, a capability displayarea 270 for graphically displaying the results of a capability studycomprises one or more explanatory statements 272 and one or more databoxes 274. In at least one embodiment, explanatory statements 272 serveto outline the potential steps of a process improvement strategy forreducing the number of defective parts produced in a process. Similarly,data boxes 274 may respectively contain data relating to eachexplanatory statement 272. For example, in the exemplary embodimentillustrated in FIG. 4, explanatory statements 272 and data boxes 274indicate that the process, as it is currently configured (the “as is”process), will produce non-conforming material 21.94% of the time (or219419 non-conforming parts per million (ppm)). Statements 272 and boxes274 also indicate that, if a special cause (also known as anextraordinary variation or process shift) is removed from the process,the percentage of non-conforming material produced can be expected todrop to 7.4%. These statements and boxes also indicate that if theprocess is shifted so that the mean is centered between thespecification limits, this percentage can be expected to further drop to9.8%. If the special cause is removed and the process is centered,statements 272 and boxes 274 indicate that the percentage ofnon-conforming material will drop to 0.27%.

Since finding and removing the special cause of shifts and trends in aprocess is usually reasonably straightforward, in at least oneembodiment a suggestion to remove special causes is presented as thefirst explanatory statement 272 following the “as is” explanatorystatement. In addition, because a process is unlikely to stay centereduntil the cause of a shift is removed, the suggestion to center theprocess is, in certain embodiments, only presented after a suggestion toremove these special causes has been presented to the user. Byorganizing explanatory statements 272 in this manner, display area 270thus provides a user with a simple, straightforward and effectivestrategy for reducing the amount of non-conforming material produced bya process.

FIG. 5 is an illustration of an additional graphical representation ofstatistical data that may be displayed in main display area 210. Morespecifically, FIG. 5 is an illustration of an exemplary process behaviorchart configured to graphically represent trended data for display inmain display area 210. Generally speaking, many processes exhibit atrend that is a natural part of the process. For example, the largesthole a drill bit will ever drill is the first one it drills. As the bitages and material is slowly ground away, the bit shrinks and the holesit drills become smaller. Accordingly, a time series representation ofthe hole size drilled by this bit will exhibit a natural trend ofgradually reduced hole sizes.

In the exemplary embodiment illustrated in FIG. 5, a trended behaviorchart 280 comprises a trendline 282 serving as a centerline of chart 280and two parallel limit lines 284 having the same slope as and positionedto surround trendline 282. While the centerline in conventional processbehavior charts is typically drawn parallel to the horizontal axis ofthe chart, trendline 282 serving as the centerline of chart 280 may bedrawn with a slope that follows an identified process trend. In at leastone embodiment, trendline 282 is only drawn with a slope that followsthe identified trend after the trend has been verified as real (i.e., atrend that is not attributable to chance), as determined by methods wellknown to those of skill in the art. Once the trend has been verified asreal, the slope of trendline 282 may be sloped in accordance with thistrend. Parallel limit lines 284 may also be drawn to have the same slopeas trendline 282, instead of merely providing limit lines that are drawnparallel to the horizontal axis of the chart.

Because conventional process behavior charts and control charts fail toaccount for process trends, a user may incorrectly identify a data pointas falling outside of a specification limit when, in reality, theobjectionable data point may fall within the accepted limits of aprocess trend. Thus, because the slopes of centerline 282 and limitlines 284 may be drawn to follow that of a real process trend, chart 280enables a user to quickly and correctly determine whether data points ina trended data set fall within the required limits (as indicated bylimit lines 284). Further, because the slopes of trendline 282 and limitlines 284 are only sloped to follow the slope of an identified processtrend once the trend has been verified as real, a user viewing chart 280can be confident that the trended slope in chart 280 is accurately andappropriately represented.

FIG. 6A is an illustration of an exemplary graphical user interface forsplitting a process behavior chart. When a process is undergoing change,it is common practice to split a process behavior chart relating to thischanging process into segments corresponding to the various stages ofthe changing process. Splitting a process behavior chart in this mannerenables a user to quickly and simply evaluate the output of the stagesof the changing process independent from one another. For example, asillustrated in FIG. 6B, a behavior chart illustrating the output of aprocess may be split into three segments representing the output of theprocess prior to a change in the process (the “old” process), during achange in the process (the “transition” period), and after a change inthe process has occurred (the “new” process). FIG. 6A is an illustrationof an exemplary, simplified graphical user interface 290 for allowing auser to split a process behavior chart in this manner.

As seen in FIG. 6A, a user may request the creation of a split behaviorchart by using a user input device, such as a mouse, to check selectionbox 292. In at least one embodiment, some form of explanatory label,such as the “Chart Split” label illustrated in FIG. 6A, is displayednext to selection box 292 to indicate its function. Once selection box292 has been checked, the user may be prompted to specify the beginningpoint of each desired segment and a name for each segment. A chartsimilar to exemplary chart 300 in FIG. 6B may then be generated.

FIG. 6B is an illustration of an exemplary process behavior chartsegmented or split in accordance with the values input by a user in theinterface illustrated in FIG. 6A. As seen in this figure, exemplarychart 300 comprises a first segment 302, a second segment 304, and athird segment 306. In general, the beginning points and names of each ofthese segments correspond to the values entered by the user in databoxes 294 of interface 290. For example, second segment 304 in FIG. 6Bis labeled “transition” and begins at data point 13 in accordance withthe values specified in data boxes 294. Interface 290 thus enables auser to quickly and easily split a process behavior chart into one ormore desired segments, as illustrated in FIG. 6B.

In certain embodiments, a user may desire to more closely examine orotherwise manipulate the underlying data relating to a data point orpoints graphically displayed in a chart or graph displayed in maindisplay area 210. This may be accomplished in the quality analysisprogram and graphical user interface of the present invention in manyways. For example, as illustrated in FIG. 7A, a user may “lasso” orotherwise select a data point, such as data point 312 in chart 310,using a user input device (such as a mouse) connected to computer system100 in a manner well known to those of skill in the art. The underlyingdata corresponding to the selected data point or points may then behighlighted, flagged or otherwise emphasized in a data table containingthe data relating to the process. For example, as illustrated in FIG.7B, a data value 317 in data table 315 corresponding to selected datapoint 312 in chart 310 may be flagged, highlighted or otherwiseemphasized to indicate that this data value corresponds to the datapoint selected by the user. The highlighted or flagged data value 317 indata table 315 may then be further examined or manipulated by the userusing various tools of a quality analysis program. For example, the usermay omit the selected data point 312 and data value 317 from graph 310,or may leave data point 312 in graph 310 but omit data value 317 fromfurther calculations, such as the calculation of the centerline andnatural process limits of graph 310. By highlighting or flagging theunderlying data corresponding to a selected data point in this exemplarymanner, the graphical user interface of the present invention enables auser to quickly and easily identify and further manipulate variouspoints of graphically displayed data.

Similarly, in many embodiments of the present invention, when datapoints are selected in a first graphical representation by a user, thesesame data points may be highlighted, colored, flagged, or otherwiseemphasized in subsequent graphical representations. For example, as seenin FIG. 8, a group of data points 321, 323 respectively displayed infirst and second plots 320, 324 may be highlighted, colored, flagged, orotherwise emphasized to indicate that these data points correspond tothe data points 325 “lassoed” or otherwise selected by a user in a thirdplot 324 using a user input device (such as a mouse). In this example,the results of a three-way factorial experiment are displayed in FIG. 8,with noise as the output variable and transistor type, feedback, andemitter current as the input variables. By highlighting, coloring, orotherwise emphasizing the data points that correspond to the selecteddata points 325 in plot 324, the user can quickly and easily see thatthe noise that occurred when 7 mA of emitter current was applied alsooccurred with no feedback (as illustrated by points 323 in plot 322),and with the 2N2219A transistor (as illustrated by points 321 in plot320). Accordingly, this feature allows a user to quickly and correctlyidentify and analyze data points across a number of graphicalrepresentations.

FIG. 9 is an illustration of an exemplary graphical user interface for adynamic process map. In at least one embodiment, a dynamic process map330 comprises one or more process steps 332, 334 representing thegeneral flow of a process. In many embodiments, one or more user-definedKey Process Input Variables (KPIVs) are categorized in input boxes 336as inputs in the process, while one or more user-defined Key ProcessOutput Variables (KPOVs) are categorized in output boxes 338 as outputsof the process. As illustrated in FIG. 9, the arrangement and number ofthe process steps in process map 330 may be dynamically manipulated by auser as desired. For example, in certain embodiments the order of aprocess step (such as process step 334) may be altered by a user byselecting the step using a user input device (such as by right-clickingthe step using a mouse). A dialog box, such as dialog box 340,containing a number of actions that may be performed on the selectedstep may then be displayed in proximity to the selected step. A user maythen manipulate the selected step by selecting one of the actionsdisplayed in dialog box 340. In certain embodiments, this configurationenables a user to move a step up or down within process map 330, deletethe step from the process, or add a new step to the process, althoughmany other actions are conceivable. This exemplary configuration thusenables a user to quickly and easily manipulate and dynamically arrangethe steps in process.

FIG. 10 is an illustration of an exemplary process map 330 incorporatinga step efficiency value. In the exemplary embodiment illustrated in thisfigure, a step efficiency icon 350 and a step efficiency value 352 areincorporated into and displayed within each step 332 in process map 330.In at least one embodiment, step efficiency value 352 numericallyrepresents the efficiency of the step in terms of the value it adds tothe process as a whole. More specifically, step efficiency value 352numerically indicates whether the time spent in performing the selectedstep is appropriate in light of the value ultimately added by this stepto the process as a whole, as determined by the user.

In at least one embodiment, the values necessary for computing stepefficiency value 352 are entered by a user in an efficiency inputinterface 354. Generally speaking, efficiency input interface 354comprises one or more input boxes 356 positioned next to one or moreexplanatory labels 358. To enter the required values, a user may selectstep efficiency icon 350 using a user input device, such as a mouse,which triggers the display of efficiency input interface 354. The usermay then input the desired values for the “Value Added” time and the“Total” time in input boxes 356 using a user input device, such as akeyboard. In general, the “Value Added” is a theoretical valuerepresenting the amount of time to be allotted for completing the stepin light of the value the step contributes to the process as a whole. Inat least one embodiment, the “Value Added” time is assigned by a user byevaluating the value of the process, versus the value of the process asa whole, using methods known to those of skill in the art. On the otherhand, the “Total” time is the actual amount of time currently requiredto complete the step. Once these values have been entered by the user,step efficiency 352 may then be automatically computed by dividing the“Value Added” time by the “Total” time of the process. As the “Total”time required to complete a step decreases, step efficiency value 352increases, indicating an increase in efficiency.

Displaying step efficiency value 352 in process map 330 in this mannerthus enables a user to quickly and efficiently determine the relativetime to be allocated to each step in a process. Advantageously, byincorporating a step efficiency value 352 into process map 330 in thismanner, the design and flow of a process map may be simplified and therelative value of each step in this process may be easily determined.

FIG. 11 is an illustration of an additional graphical representation ofstatistical data that may be displayed in main display area 210. As seenin this figure, an exemplary cause and effect Pareto 370 chart maycomprise one or more columns 374 vertically extending above one or moreKey Process Input Variables (KPIVs) 372 positioned along the chart'shorizontal axis. In general, chart 370 graphically represents thestrength of the influence that each input variable 372 is likely to haveon a process. The strength of an input variable's influence is generallydetermined based on a score computed using a cause and effect matrix, asis known to those of skill in the art. The higher the score, the moreinfluence the input variable is likely to have. To help a user of thequality analysis program quickly and easily determine the mostinfluential input variables, the score of each input variable 372 may begraphically represented by columns 374 in chart 370, sorted from largestto smallest.

As seen in FIG. 11, similar to the graphical representation in FIG. 7A,a user may “lasso” or otherwise select one or more input variables 372in chart 370 using a user input device, such as a mouse. The selectedinput variables 372 may then be removed from consideration or merelyflagged by pushing a button on the user input device. In at least oneembodiment, chart 370 is automatically generated and displayed in maindisplay area 210 once the score for each input variable 372 has beencomputed. In other embodiments, chart 370 is generated and displayedwhen a user selects push button 376 using a user input device.

FIG. 12 is an illustration of an exemplary graphical user interface fora data table. In at least one embodiment, an exemplary data table 380containing the data values of one or more variables from one or morestatistical experiments is displayed in main display area 210. Datatable 380 may comprise one or more column headings 382 and one or morerow headings 384. While row headings 384 may be labeled or designated inany number of manners, in at least one embodiment each row heading 384is labeled to correspond to a run in a multi-run statistical experiment.

As is well known to those of skill in the art, the type of statisticaltest that is appropriate for a particular situation depends, to a greatdegree, on the types of variables to be tested. For example, if a userdesires to run an experiment to determine the relationship between aninput variable and an output variable when both variables areinterval/ratio type intervals, a regression-type statistical experimentshould be performed. Similarly, if more than one interval/ratio typeinput variable is to be investigated, then multiple linear regressionexperiments are required. When, however, the input variable iscategorical and the output variable is of the interval/ratio type, thenan ANOVA test is necessary. In addition, when multiple categorical inputvariables are of interest, then a multi-variate ANOVA test is required.

To aid in the selection and application of appropriate statisticaltools, in at least one embodiment data table 380 allows a user todesignate the type of variable contained in each column in table 380. Inparticular, a user may designate whether a column of variables is to bedesignated as: (1) an output variable (Y variable); (2) aninterval/ratio input variable (X variable); or (3) a categorical inputvariable (Xc variable), each of which is well known to those in the art.Generally speaking, a user may designate these variable types in anynumber of ways. For example, a user may designate the type of variablecontained in each column by selecting a column heading 382 using aninput device (such as a mouse) and by selecting one of the optionsdisplayed in a pop-up menu 386. In the exemplary embodiment illustratedin FIG. 12, the input variable type “Xc” has been selected in pop-upmenu 386, indicating that the column labeled “Soil Type” has beendesignated as a categorical input variable.

By allowing a user to designate the variable type for each column indata table 380, the process of selecting the appropriate statisticaltool for the experiment may be simplified and automated. For example,when one or more input variables in data table 380 are designated “X”(representing an interval/ratio input variable), further analysis andexperiments performed on this data may automatically be treated asregression-type problems. Further, when one or more input variables indata table 380 are designated “Xc” (representing a categorical inputvariable), further analysis and experiments performed on this data mayautomatically be evaluated using ANOVA. If both types of input variablesare chosen, then a model that allows both regression-type and ANOVAanalysis at once, as is known in the art, may be automatically selected.

FIG. 13 is an illustration of an exemplary graphical user interface forthe design of factorial experiments. In certain embodiments, a factorialexperiment design interface 390 capable of being displayed in maindisplay area 210 generally comprises one or more feature selectionregions and an output table 402. In the exemplary embodiment illustratedin FIG. 13, interface 390 comprises a first feature selection region392, a second feature selection region 394, a third feature selectionregion 396, a fourth feature selection region 398, and a fifth featureselection region 400. In first feature selection region 392, a user mayselect the type of experiment to be run by selecting from one or moreoptions displayed in region 392 using a user input device, such asmouse. Although many choices may be displayed in first feature selectionregion 392, in at least one embodiment the user may select from thefollowing options: (1) a ½ faction 2^(K) screening operation; (2) a sixor more factor screening operation; (3) a 2^(K) factorial experiment;(4) a full factorial experiment; and (5) a central compositeoptimization operation.

In second feature selection region 394, the user may point to and selecta cell in the table that corresponds to the number of factors to beinvestigated and the number of runs to be performed per replicate. Inthis example, a selected cell 395 corresponds to three factors and eightruns per replicate, which is a fully crossed design (as indicated by theletter “F” in selected cell 395). As known to those of skill in the art,a fully crossed design means that all possible combinations will bechecked. The resolution of cells that correspond to designs that areless than fully crossed are indicated by other roman numerals, such as,for example, I, II, III, IV, and V.

In third feature selection region 396, the number of factors (inputvariables) selected in second feature selection region 394 (three inthis case) are displayed in a grid. The names of each of these inputvariables may be designated, as desired, by the user using a user inputdevice (such as a mouse and a keyboard). In addition, the values foreach of these input variables may be designated by the user, as desired.

In fourth feature selection region 398, the user may enter an acceptablealpha risk (“Alpha,” often 0.05, as known in the art), the minimumeffect that is of interest (“Effect”), and an estimate of the standarddeviation of the process (“Sigma”). As discussed in greater detailbelow, these input values may be used in calculating the “power” of anexperiment. In fifth feature selection region 400, the user may specifythe number of blocks or centerpoints to be included in the experiment.The user may also add replicates as desired. As is known to those ofskill in the art, the higher the number of replicates is, the higher the“power” of the experiment and the more likely the experiment will beable to accurately detect change (if present) in a process.

Once the user has specified all necessary inputs, the designed test maybe generated by selecting pushbutton 404, which, in certain embodiments,is labeled “Generate Design.” The correct combinations of inputvariables to carry out the experiment may then be automatically computedand displayed in output display 402. Simplified interface 390 thusallows the user to design and generate a statistical test or experimentusing a minimal number of steps displayed on a single page. Thisexemplary design of interface 390 thus decreases the complexity involvedin convention experiment design programs, resulting in increasedinterpretability and time savings.

In at least one embodiment, exemplary experiment design interface 390also comprises a gas gauge type graph 406 for graphically representingthe results of a statistical test or experiment. Similar to gas gaugegraph 250, in certain embodiments gas gauge graph 406 comprises a scaleand a pointer, as illustrated in FIG. 13. In addition, various portionsof the scale in graph 406 may be colored coded to generally representvarious statistical values. For example, a right hand portion G of thescale may be colored green, a portion Y next to this portion may beyellow, with the remaining portion R colored red. Configured in thismanner, gas gauge 406 may be used to graphically represent variousstatistical data; including, for example, the “power” of a statisticaltest. As known in the art, the “power” of a statistical test, such as afactorial experiment, is the probability of detecting change (ifpresent) in a process. In at least one embodiment, interface 390 and gasgauge graph 406 are used to compute and display the power of a processprior to performing the test to help a user determine whether enough,but not too many, resources have been dedicated to the test to ensureits accuracy and reliability.

According to certain embodiments, the power of a test is computed basedon inputs supplied by a user in interface 390. In particular, the powerof a test may be computed, using methods well known to those of skill inthe art, based on the “Alpha,” “Effect,” and “Sigma” values entered infourth feature selection region 398, the number of factors and base runsspecified in second feature selection region 394, and the number ofreplicates, centerpoints, and blocks specified in fifth featureselection region 400. While the value of the power computed in thismanner may be numerically displayed in output display 402, in at leastone embodiment this power value is graphically represented using gasgauge 406. In particular, gas gauge 406 may be used to quickly andsimply indicate whether the computed power value of the statistical testto be performed is high enough to ensure the test's accuracy andreliability. For example, in at least one embodiment gas gauge 406 maybe configured such that when the computed power value indicates asufficiently high beta-1 probability of detecting change (if present) ina process, the pointer in gauge 406 is positioned within green portionG. Similarly, gas gauge 406 may be configured such that when thecomputed power values indicates that there is a low likelihood that thetest will be able to accurately detect a change when present, thepointer in gauge 406 may be positioned within red portion R. Byexamining gas gauge 406, a user may thus be able to quickly and easilydetermine whether the designed experiment exhibits a sufficient powervalue to be accurate and reliable.

Still further, the graphical representation of the power value in gasgauge 406 may be automatically and dynamically generated based on theuser's entries in interface 390. In other words, in at least oneembodiment the power value of the test to be performed may be computedand displayed in graph 406 as each value is entered by the user ininterface 390. For example, upon entry of the number of replicatesdesired in feature selection region 400, the power value of the test maybe computed based on this value and displayed in graph 406. As variousvalues in interface 390 are changed, the pointed in graph 406 may changeposition accordingly to indicate the resulting change in power. In manyembodiments, this exemplary and simplified configuration enables a userto quickly and easily determine the variable values and combinationsthat will result in a sufficiently high power value. For example, since,as is known in the art, an increase in the number of residuals resultsin increased power values, a user may gradually increase the number ofresiduals selected in feature selection region 400 until the pointer ingas gauge graph 406 falls safely within the green portion G of thescale.

In addition, in at least one embodiment the values of the fourcapability indices Cp, Cpk, Pp, and Ppk may be generated and displayedin main display 210 as an additional output of a factorial experimentdesigned using interface 390 in FIG. 13. As is well known to those inthe art, four inputs are required to calculate the four capabilityindices discussed in connection with FIG. 4 above; namely, thespecification limits of the process, the mean of the process, thestandard deviation of the process as determined by the sum of squaresmethod, and the standard deviation of the process as determined by themoving range method. Although these four inputs and indices areconventionally generated and calculated as part of a discretestatistical test, in at least one embodiment these capability indices(namely, Cp, Cpk, Pp, and Ppk) are calculated as an output of and inconjunction with another statistical test, such as a factorialexperiment. As known by those of skill in the art, factorial experimentsare commonly used to test each variable in a multi-interactionenvironment to determine whether the tested variable has a realinfluence on the process as a whole or not. Factorial experiments alsopredict the mean of the output variable for each possible combination offactors (also known as states).

In computing these capability indices, in at least one embodiment themean of the output variable predicted by the factorial experimentdesigned in FIG. 13 for each of a desired number of possiblecombinations serves as the first input for a capability index. Inaddition, because the specification of a process and the standarddeviation of the process (as determined by the sum of squares method andthe moving range method) are either available or readily ascertainableusing known methods, the second, third and fourth inputs necessary tocalculate the four capability indices may be provided. Thus, inaccordance with at least one embodiment, the values of the fourcapability indices Cp, Cpk, Pp, and Ppk may be generated and displayedin an index output box 408 as an additional output of a factorialexperiment designed using interface 390.

As will be appreciated, this exemplary configuration enables a user tomake a reasonable prediction of process capability as part of afactorial experiment before a process is set up or changed. This savesthe time and expense associated with generating and measuring additionalsamples to estimate the capability indices. In addition, since, as knownto those in the art, Cpk is relatively insensitive to non-homogeneitywhile Ppk is sensitive, a user may compare the values of Cpk and Ppkderived from residuals, to obtain a useful numerical estimate of thedegree of homogeneity of the residuals.

FIG. 14 is an illustration of an exemplary graphical user interface 410for enabling a user to automatically reduce insignificant variables inan experiment. In at least one embodiment, exemplary interface 410comprises an interaction selection region 411, an alpha selection region412, pushbuttons 414 and 416, and an output display 416. While thisinterface may generally be configured to perform any number ofstatistical tasks, in certain embodiments this interface is configuredto allow a user to automatically eliminate “weak” variables from aprocess; i.e., those variables whose interactions can be attributedmerely to random chance. In general, the “strength” of each variable ina test may be determined using any number of statistical tests;including, for example, by using a factorial experiment. According tomany embodiments, the strength of each variable is represented as a Pvalue, discussed in greater detail above. As is well known in the art,lower P values generally indicate “stronger” variables.

In at least one embodiment, interface 410 enables a user to specify thenumber of desired interactions in a process via interaction selectionregion 411. A user may also specify the highest allowable P value foreach variable in the process in alpha selection region 412. For example,as illustrated in FIG. 14, a user may specify that only variablesdetermined to have P values of under a specified value, such as 0.10,are to be considered. After specifying these values, a user may instructthe program to run the experiment by pressing pushbutton 414 (labeled,in one example, “Calculate”) using a user input device. The results ofthe experiment may then be displayed in output display 416. Further, allvariables and interactions exhibiting P values higher than the valuespecified in alpha selection 412 may be automatically removed fromoutput display 416 (or designated as noise) by selecting pushbutton 418(labeled, in one example, “Auto Reduce”). As illustrated in outputdisplay 416, only those variables and interactions having P values 420of less that 0.10 remain. In at least one embodiment, these P values 420may be highlighted, color coded, flagged, or otherwise emphasized toindicate that they fall within the specified P value tolerance.

FIG. 15 is an illustration of an additional graphical representation ofstatistical data that may be displayed in main display area 210. Incertain embodiments, an exemplary scree plot 430 capable of beingdisplayed in main display area 210 generally comprises one or morecolumns of data 432 arranged in descending order by their height.Although scree plot 430 may be used to graphically display the resultsof any number of statistical tests, according to one aspect scree plot430 graphically displays the results of a sum of squares or normalizedsum of squares test (both of which are well known to those in the art)in descending order. When the data of these tests is arranged in thismanner, the height of one or more of the later columns is typicallyinsignificant when compared to the heights of the earliest columns. Inmany situations, this configuration looks very similar to scree (orrubble) at the base of a cliff; thus giving rise to the “scree”nomenclature.

By graphically presenting the results from a statistical test in thismanner, a user of the program of the present invention can visually andquickly determine those variables that are relevant to the process andthose that are not. For example, in the exemplary embodiment illustratedin FIG. 15, a user may be able to quickly ascertain that variables C, G,E and A are likely of negligible concern, as opposed to variables D, F,and B, which are likely of significant concern. In addition, in manyembodiments, the heights of columns 432 form a sort of “elbow” ordrastic division between two adjacent columns. For example, as seen inFIG. 15, the drastic height differential between column B and column Cresults in what resembles an “elbow.” When an elbow such as this isformed in scree plot 430, a user may logically eliminate any variablessubsequent to the elbow (namely, variables C, G, E and A) fromconsideration as weak or inactive variables.

FIGS. 16A-16C are illustrations of additional graphical representationsof statistical data that may be displayed in main display area 210. Moreparticularly, FIGS. 16A-1C illustrate an exemplary manner fordetermining and graphically displaying the significant variables orinteractions in a fractional factorial experiment. As is known in theart, a fractional factorial experiment is a test in which only anadequately chosen fraction of the treatment combinations required forthe complete factorial experiment is selected to be run. Whilefractional factorial experiments typically result in significantresource savings by reducing the number of runs required in anexperiment, the trade-off for this savings benefit is an inability toobtain an estimate of the main effect of a variable that is separatefrom an interaction effect for other variables in the process. In otherwords, the reduced number of runs in a fractional factorial experimentmay result in the main effect estimate for an exemplary variable A being“confounded” with the estimate of the interaction effect for variables Band C (this effect is also known as “aliasing”).

In at least one embodiment, as seen in FIGS. 16A-B, a display area 440capable of being displayed in main display area 210 is configured todisplay one or more variables 442, one or more interactions 444, and thecoefficient 446 and sum of squares value 448 (SSQ) for each variable 442in a fractional factorial experiment. Generally speaking, thecoefficient of a variable typically indicates the magnitude anddirection in which each variable moves a process. For example, acoefficient of −5 indicates that for each unit increase in the variable,the output of the process decreases by 5. Conversely, a coefficient of+5 indicates that for each unit increase in the variable, the output ofthe process increases by 5. However, with respect to the coefficientvalues 446 displayed in FIG. 16A, because fractional factorialexperiments involve relatively few observations, as discussed above,determining whether the magnitude of coefficient value 446 is caused byvariable 442 alone, or whether this result is instead caused by one ormore interactions 444, requires additional analysis.

FIG. 16B illustrates an exemplary manner for determining which variables442 or interactions 444 in a process are responsible for the effectsillustrated by the coefficient values 446 in FIG. 16A. In at least oneembodiment, the signs of each interaction 446 in FIG. 16A (which are, inthis embodiment, all negative) are reversed in FIG. 16B (in this case,to positive values). The coefficient values 446 for each variable 442are then computed again and displayed in FIG. 16B. As seen in theexemplary embodiment illustrated in this figure, the coefficients values446 for variables B, D, E and G retain the same sign in FIG. 16B as theyhad in FIG. 16A (i.e., those that were positive remained positive, whilethose that were negative remained negative). The most likely explanationfor this is that the effects indicated by the coefficients for thesevariables were actually caused by the variables themselves, and not byone of the interactions associated with the variables. In contrast,because the signs of the coefficient values 446 for variables A, C, Fand G were reversed in FIG. 16B, this most likely indicates that theeffects indicated by the coefficients for these variables were caused byone of the interactions associated with these variables, instead of bythe variables themselves. Thus, by keeping track of which coefficientvalues switched signs in this manner, a user may be able to determinewhich variables and interactions may be removed as statisticallyinsignificant.

In an additional embodiment, a simplified manner of graphicallyrepresenting the results of FIGS. 16A and 16B is illustrated in FIG.16C. As shown in this figure, in certain embodiments an exemplary graph450 illustrates, in column format, the magnitude of the measured effectof each variable 440 in FIGS. 16A and 16B. In addition, in at least oneaspect of the invention, one or more variables or interactions arecircled (as indicated by circles 452), highlighted, color coded, flaggedor otherwise emphasized in FIG. 16C to indicate to a user that thesevariables may be removed as insignificant. As will be appreciated bythose of skill in the art, in at least one embodiment only thosevariables whose coefficient signs changed in FIG. 16B (i.e., thosevariables whose effects may be attributed to other interactions) arecircled. Similarly, only those interactions whose coefficient signsremained the same in FIG. 16B may be circled in FIG. 16C. By emphasizingthose variables and interactions that may be removed as beingstatistically insignificant, FIG. 16C enables a user to quickly andeasily determine which variables may be removed as insignificant,without having to keep track of which coefficient values 446 in FIGS.16A and 16B switched signs or remained the same. FIG. 16C thussimplifies the interpretation and analysis of fractional factorialexperiments for a user, enabling quick and efficient interpretation ofthe results of the same. FIG. 16C may either be displayed alone or incombination with FIGS. 16A and 16B.

FIGS. 17A and 17B illustrate additional graphical representations ofstatistical data that may be displayed in main display area 210. As seenin FIG. 17A, in at least one embodiment an exemplary explanatory box 460generally comprises one or explanatory statements 462 positioned next toone or more statistical data values 464. In many embodiments,statistical data values 464 represent outputs of various statisticalexperiments, while explanatory statements 462 contain text that explains“in plain English” the significance of the displayed statistical datavalues 464.

For example, in the exemplary embodiment illustrated in FIG. 17A,explanatory box 470 has been configured to help a user interpret theresults of a T-test. As is known in the art, a T-test is a statisticaltool that is used to determine whether the means of two samples aredifferent or not. This is determined by first accepting the hypothesisthat the two numbers are the same. If the T-test returns a p-value ofmore than 0.05, then the hypothesis is accepted, and the sets of numberscan be considered the same; otherwise, the sets of numbers areconsidered to be different. To help a user interpret the results of sucha T-test, in the example illustrated in FIG. 17B a first explanatorystatement 462 consists of text that indicates that a user has a 0.003chance (as indicated by the P value of data value 464) of being wrong ifthe user accepts the hypothesis that the actual difference between thepopulations represented by the samples in the T-test is less than zero(or some other pre-determined number). Similar explanatory statements462 are displayed for the remaining hypotheses. Based on theseexplanatory statements, a user may quickly and simply determine that thedifference between the means of the two samples is most likely less thanzero (or some other pre-determined number).

By providing explanatory statements (such as statements 462 in FIG. 17A)relating to data values 464 in this exemplary manner, explanatory box460 allows a user to quickly and efficiently interpret the results of astatistical test. The time required for a user to interpret andsynthesize the significance of the results of statistical tests may thusbe reduced, resulting in increased efficiency and ease of use.

FIG. 17B illustrates an additional manner for graphically representingthe results of a statistical test, such as a T-test. In at least oneembodiment, an exemplary chart 470 for graphically representing theresults of a T-test comprises a plurality of distributions 472, 474 and476 illustrating the distributions of the possible alternativehypotheses of a T-test. For example, in this embodiment firstrepresentation 472 illustrates the distribution that would result if thehypothesis is accepted that the difference between the means of the twosamples tested is less than zero, second representation 474 illustratesthe distribution that would result if the hypothesis is accepted thatthe difference between the means of the two samples tested is differentthan zero, and third representation 476 illustrates the distributionthat would result if the hypothesis is accepted that the differencebetween the means of the two samples tested is greater than zero. In atleast one embodiment, a portion 473 in first representation 472 isillustrated highlighted, colored, or otherwise emphasized to illustratethe difference between the means of the two samples tested according tothe first hypothesis. Portions 475 and 477 may also be similarlyemphasized.

By graphically presenting the results of each hypothesis in theexemplary manner illustrated in chart 470, a user operating the qualityanalysis program may easily and quickly interpret and understand thesignificance of the results of the test. As with explanatory box 360,the time required for a user to interpret and synthesize thesignificance of the results of statistical tests presented in thismanner may thus be reduced, resulting in increased efficiency and easeof use for users.

FIG. 18 illustrates an additional graphical representation ofstatistical data that may be displayed in main display area 210. As seenin this figure, in at least one embodiment an exemplary assumption testdisplay area 480 comprises a first behavior chart 482, a second behaviorchart 484, and a histogram 486. As is known to those of skill in theart, the assumption of homogeneous data is a critical assumption in manystatistical tests. Generally speaking, data are deemed homogenous whenno strong, unknown variables are present in the data of a process. Thus,in certain embodiments, this assumption of homogeneity may be tested bycreating a behavior chart of the data involved in the process.

As seen in FIG. 18, exemplary assumption test display area 480represents a manner for graphically displaying the results of one ormore tests for homogeneity. In at least one embodiment, when a singleinput variable is involved in a process, the original data in theprocess is graphically displayed in a process behavior chart (such asbehavior charts, such as 482, 484) in order to evaluate whether thisoriginal data is homogeneous. On the other hand, when more than oneinput variable is involved in the process, the residuals of the test areinstead charted in the process behavior chart. As detailed above, a“residual” is the value from an original data set after the effects ofthe variables in this set have been removed.

In the embodiment illustrated in FIG. 18, a two sample T-test involvinga single input variable was performed. Thus, because only a single inputvariable was involved in the T-test, the original data from each samplein the test may be plotted, instead of using the residuals thereof. Forexample, first behavior chart 482 in FIG. 18 graphically represents thebehavior of the original data from a first sample of the T-test, whilesecond behavior chart 484 graphically represents the behavior of theoriginal data from the second sample of the T-test. As seen in firstchart 482, each of the data values from the first sample fall within thespecified limits, and may therefore be assumed as homogeneous. On theother hand, as seen in second chart 484, the last data point in thesecond sample falls outside of the specified limits, such that this datacannot be assumed as homogeneous, nor can it be relied upon as accurate.

Assumption test display area 480 thus enables a user to quickly andaccurately determine whether the data in a process is homogeneous andthus reliable. Although in certain embodiments the type of process chartto be created and displayed in display area 480 (i.e., whether a processchart is to be created of the original data or the residuals of thedata, as explained above), is determined and specified by a user, in atleast one embodiment this decision is automatically made by a qualityanalysis program based on the number of input variables involved in theprocess, as discussed above.

In addition, while in certain embodiments process behavior charts (suchas first and second behavior charts 482, 484) are generated anddisplayed separately from a histogram generated based on the same data(such as histogram 486), in at least one embodiment a histogram isdisplayed concurrently with, and in the same display area as, one ormore process charts. For example, as seen in FIG. 18, a histogram 486graphically representing the data presented in charts 482 and 484 may begenerated and displayed in the same display area 480. Although thecombination of histograms and process charts in a single display areamay occur for any variety of statistical tests and experiments, in atleast one embodiment a histogram for a capability study issimultaneously generated and concurrently displayed with a process chartbased on the same capability study.

FIG. 19 is an illustration of an exemplary graphical user interface forenabling a user to determine an appropriate power value, differencevalue, and sample size for a statistical test. In the exemplaryembodiment illustrated in this figure, an input interface 490 comprisesa plurality of input boxes (such as, for example, a sample size box 492,a difference box 494, a power box 496, a sigma box 498, and an alpha box500), a selection region 502, a slider 504, and a pushbutton 506.Generally speaking, the sample size (N) of a test may be entered insample size box 492, the difference value (delta) of a test may beentered in difference box 494, the power value (1-beta) of a test may beentered in power box 496, the standard deviation (sigma) of anundisturbed process may be entered in sigma box 498, and thesignificance level (alpha) of a test may be entered in alpha box 500.

According to certain embodiments, a user may request the calculation ofan unknown variable (such as, for example, the sample size, differencevalue, or power value of a test) by positioning slider 504 using a userinput device to point to one of the boxes representing the variabledesired to be calculated. For example, as seen in FIG. 19, slider 504may be positioned next to sample size box 492 when the sample size of atest is unknown to the user. A user may then request the calculation ofthe estimated sample size of the test by selecting pushbutton 506(which, in some embodiments, is labeled “Calculate”) after havingentered the known values for each remaining input box 494, 496, 498, 500and after having specified, in selection region 502, whether the processis one or two sided. The sample size of the test may then be calculatedand displayed in box 492 using methods known to those of skill in theart.

Accordingly, the simplified interface of input interface 490 allows auser to quickly and easily request the computation of an unknownvariable in light of other known variables. Advantageously, thisconfiguration enables a user to quickly and easily determine the samplesize that will be required to produce the degree of statisticalcertainty required for reliable interpretation (i.e., the power or1-beta value of the test). While input interface 490 may be adapted foruse in connection with any variety of tests and experiments, in certainembodiments this interface is tailored for use in connection withT-tests and proportion tests.

FIG. 20A is an illustration of an exemplary graphical user interface forenabling a user to sort the results of a test based on an inputvariable. FIGS. 20B and 20C are illustrations of exemplary processbehavior charts generated based on input variables selected in FIG. 20A.As detailed above in connection with FIG. 12, in at least one embodimentof the present invention a user may designate the type of variablecontained in each column in a data table (such as data table 380 in FIG.12). Similarly, in an additional embodiment of the present invention, auser may sort data in a data table by variable, or may request thegeneration of a process chart for a specific variable or variable typein a data table. For example, as seen in FIG. 20A, a user may triggerthe display of a main pop-up menu 512 by selecting a variable containedin a data table 510 using a user input device, such as a mouse. Usingthe input device, the user may then position a cursor over the option“Sort,” which triggers the display of a secondary pop-up menu 514. Theuser may then cause the data in data table 510 to be sorted (in eitherascending or descending order) based on the selected variable byselecting either of the options (in this case, “Ascending” and“Descending”) displayed in secondary pop-up menu 514. For example, ifthe user chooses to sort, in ascending order, a set of data based on theinput variable “Average Temperature,” the runs of data in the data tablewill be arranged, in ascending order, by the value of their averagetemperature.

In addition, once the data in data table 510 has been sorted accordingto a selected variable, a behavior chart (such as behavior charts 516,518) based on the outputs of the sorted variables may be generated anddisplayed in main display area 210. These behavior charts may begenerated for categorical variables (as is the case for chart 516 inFIG. 20B), or interval/ratio variables (as is the case for chart 518 inFIG. 20C). Advantageously, by sorting and graphically charting datavalues based on a selected variable, a user may discover trends thatmight otherwise have been missed when displayed in conventional manners.

FIG. 21 is an illustration of an additional graphical representation ofstatistical data that may be displayed in main display area 210. As seenin this figure, in at least one embodiment a graphic 520 comprises alower specification limit 522, an upper specification limit 524, a loweruncertainty zone 526, and an upper uncertainty zone 528. Generallyspeaking, graphic 520 graphically illustrates the amount of uncertaintypresent in a statistical test or experiment. As is known to those ofskill in the art, the measurement systems used to measure the outputs ofprocesses can only inherently measure values to a finite amount. Valuesfalling below this finite amount are thus indistinguishable to themeasuring system. The relative width of uncertainty in such ameasurement system may thus be graphically represented using uncertaintyzones 526, 528. For example, the larger the width of an uncertainty zoneis relative to the width between specification limits 522, 524, the lesslikely the measurement system will be able to accurately and reliablydistinguish non-conforming material from conforming material.Conversely, the smaller the widths of the uncertainty zones are relativeto the distance between specification limits 522, 524, the more likelyit is that the measurement system will be able to accurately distinguishbetween conforming and non-conforming material. By graphicallyrepresenting the relative width of the uncertainty of a measurementsystem in this exemplary manner, graphical representation 520 thusenables a user to quickly determine the relative reliability of ameasurement system.

FIG. 22 is an illustration of an additional graphical representation ofstatistical data that may be displayed in main display area 210. As seenin this figure, in at least one embodiment a subgroup display area 530comprises a subdivided behavior chart 535, an S chart 540, a subgroupsize input box 542, and a subgroup column 544. According to thisexemplary embodiment, when a user suspects that operator controldifferences are contributing to undesirable process variation, the usermay de-select a check box next to input box 542 using a user inputdevice, such as a mouse. In response to the user's de-selection of thecheck box, a subgroup column 544 listing the operators by variable type,such as, in this example, by operator, may be displayed. A user may thenrequest the generation of a behavior chart 535 sorted by the selectedsubgroup (in this example, by operator) by selecting a pushbutton (notshown) displayed in interface 530. In addition, S chart 540 may begenerated to display the standard deviation of each subgroup. As isknown to those of skill in the art, an S chart is merely a plot of thestandard deviation of a process taken at regular intervals to indicatewhether there is any systematic change in the process variability. Inmany embodiments, data points falling outside of the limit lines in Schart 540 indicate a subgroup with detectably larger variation than theother groups.

As seen in FIG. 22, the centerline for each subgroup in chart 535 may becentered on the subgroup mean, while the limits lines for each subgroupmay be calculated based on the pooled variance of the data. By dividingthe data by subgroup in this manner, a user is able to quickly andsimply appreciate differences in output variables for each subgroup (inthis case, by operator). For example, in the example illustrated in FIG.22, the output differences between the third and sixth subgroups ingraph 535 are readily apparent by presenting the data in this manner,which may prompt the user to investigate the causes for thesesignificant differences in operator output.

FIG. 23 is an illustration of an additional graphical representation ofstatistical data that may be displayed in main display area 210. As seenin this figure, in at least one embodiment a Pareto display area 550comprises one or more multi-variable Pareto charts 560 and a data table570. Charts 560 may generally comprise one or more main variable columns562, one or more secondary variable columns 564, and a line graph 566.Generally speaking, main variable columns 562 graphically represent themagnitude of an effect caused by a main input variable, while secondaryvariable columns 564 graphically represent the magnitude of an effectcaused by a secondary input variable. As will be appreciated, either ofthese variables (namely, the main variable and the secondary variable)may be a categorical type input variable or an interval/ratio type inputvariable. Line graph 566 may also be displayed within main variablecolumn 562 to graphically illustrate the cumulative sum of the values ofeach column 564 blocked within main variable column 562.

For example, in the embodiment illustrated in FIG. 23, a statisticalexperiment was performed to investigate the effects of three categoricalinput variables (namely, time, temperature and pressure) on the numberof defects produced in a plastic bag sealing process. In thisexperiment, the number of defects produced in the process (per hundredruns) was measured for each of the possible settings of the categoricalinput variables (in this case, the input variable “time” was variedbetween very short, short, medium, and long time settings, the inputvariable “pressure” was varied between low, medium, high, and very highpressure settings, and the input variable “temperature” was variedbetween cool, warm, hot, and very hot temperature settings). In FIG. 23,the number of defects produced at each temperature setting is displayedin descending order as main variable columns 562. Each main column 564is also further broken down into secondary variable columns 564 toillustrate how many of the total defects for each main column 562 wereproduced at each time setting.

By graphically presenting the results of statistical tests in thismanner, a user may quickly and simply determine the relationship betweenone or more input variables and the number of defects produced in aprocess. In the exemplary embodiment illustrated in FIG. 23, forexample, the user may likely determine that higher temperatures have anadverse effect on the number of defects produced in the process sincethe number of defects reduces as the temperature is progressivelyreduced from the “very hot” to the “cold” temperature setting. The usermay also likely decide that the optimal combined time and temperaturesettings for the process are “very short” and “cool,” since the lowestnumber of combined defects were produced at these settings. Thus, bygraphically displaying one variable “blocked” within another in thismanner, exemplary charts 560 provide greater insight to a user, oftenallowing the user to catch defect causes that are interactions, ratherthan single effects.

FIG. 24 is an illustration of an exemplary final project report that maybe displayed in main display area 210. Although one or more of thepreceding graphical representations may be separately displayed ondiscrete pages, in at least one embodiment one or more of theserepresentations are presented simultaneously as a single, final reportand summary of the quality analysis project upon completion of theproject. For example, as seen in FIG. 24, one or more project stepsummary reports 602-610 may be displayed in a single screen as a finalproject report 600. Generally speaking, reports 602-610 comprise textualor graphical representations summarizing the results of each step of aquality analysis project (such as, for example, each step in a Six SigmaDefine-Measure-Analyze-Improve-Control project). Report 600 may eitherbe created and displayed at the request of a user (by, for example,selecting a pushbutton using a user input device) or automaticallygenerated by the quality analysis program at the completion of each stepin the project. Project report 600 may additionally contain notes,remarks, explanatory statements, or other comments entered by the userduring of the project. In addition to being displayed in main displayarea 210, project report 600 may also be printed by a printing device,such as a laser printer, connected to computer system 100. Bygraphically presenting the results of each step in a project in thisexemplary manner, project report 600 enables a user to quickly andsimply analyze the results of an entire project.

FIG. 25 is a chart illustrating the flow of variables between variousproject steps in a quality analysis computer program according to oneembodiment of the present invention. Since, generally speaking, each ofthe above-described project steps 222 and tools 226 in theabove-described quality analysis project are used in concert, ratherthan individually, in at least one embodiment variables created and usedin one of the projects steps or tools of the project are carried forwardfor use in subsequent steps or tools. For example, as seen in FIG. 25,in certain embodiments one or more of the variables created, defined, ormanipulated in a first project step 620 may, upon completion of thisstep, be forwarded or “rolled forward” to a second project step 622 foruse in this second step. Similarly, the variables created, defined, orotherwise manipulated in second project step 622 may be rolled forwardto third project step 624, and so on until the completion of theproject. Generally speaking, project steps 620-628 represent the stepsused by any one of various quality analysis or improvement tools orprograms; including, for example, Six Sigma, Lean, Plan-Do-Check-Act,the Toyota Production System, Total Quality analysis (TQM), and othersuch tools and programs. Project steps 620-628 may also represent one ormore of a variety of statistical tools used during the project steps ofa quality analysis project.

For example, the values of the KPIVs used during the creation of aprocess map may be carried forward and automatically entered into asubsequently created cause and effect matrix. Similarly, variablesentered in this cause and effect matrix may be automatically rolledforward for use in a subsequent tool or test, such as a failure andmodes analysis (FMEA). Further, one or more of the variables that arefound to significant in the FMEA may be assigned action items, which maybecome a part of an Action Plan in the “Improve” step of a project.Finally, the most critical items in the Action Plan may be assigned aControl Plan, which is a key part of the “Control” step of the overallproject.

By automatically carrying or rolling forward the variables created,defined, or otherwise manipulated in a project step or tool in thisexemplary manner, the task of a project leader is greatly simplified.This exemplary configuration thus saves the user from having to rememberand enter in the values of each variable created, defined, or otherwisemanipulated in each step. Various efficiency gains and realized, and theoverall ease of use of the program is increased.

The preceding description has been provided to enable others skilled inthe art to best utilize the invention in various embodiments and aspectsand with various modifications as are suited to the particular usecontemplated. This exemplary description is not intended to beexhaustive or to limit the invention to any precise form disclosed. Manymodifications and variations in the form and details are possiblewithout departing from the spirit and scope of the invention. Forexample, while a quality analysis program and graphical user interfacefor the same have been described with reference to specific types ofmenus and control objects, it will be appreciated that the practicalapplications of the invention are not limited to the disclosedembodiments. Rather, with an understanding of the principles whichunderlie the invention, its applicability to many different types ofgraphical user interfaces, and the controllable elements within suchinterfaces, will be readily apparent.

For ease of use, the words “including” and “having,” as used in thespecification and claims, are interchangeable with and have the samemeaning as the word “comprising.” It is intended that the scope of theinvention be defined by the following claims.

1. A graphical user interface for a quality analysis computer program,the interface and computer program being stored as computer-readableinstructions on a computer-readable medium, the interface comprising: afirst display area for displaying graphical representations ofstatistical data; a second display area for displaying: one or morequality analysis project steps in a quality analysis project; one ormore statistical tool categories associated with the one or more qualityanalysis project steps; one or more statistical tools associated withthe one or more statistical tool categories.
 2. The interface accordingto claim 1, further comprising one or more status indicators forgraphically displaying the status of each quality analysis project stepin the quality analysis project.
 3. The interface according to claim 1,wherein the graphical representations comprise a “gas gauge” type graphfor graphically representing results of a statistical test.
 4. Theinterface according to claim 1, wherein the graphical representationscomprise one or more slider bars for graphically representing results ofa statistical test.
 5. The interface according to claim 1, furthercomprising a capability study display area displayed in the firstdisplay area and configured to display results of a capability study,the capability study display area comprising: a first portion displayinga percentage of non-conforming material produced in a process; a secondportion displaying a percentage of non-conforming material that would beproduced in the process if an extraordinary variation is removed; athird portion displaying a percentage of non-conforming material thatwould be produced in the process if a mean of the process is centeredbetween specification limits; a fourth portion displaying a percentageof non-conforming material that would be produced in the process if theextraordinary variation is removed and the mean of the process iscentered.
 6. The interface according to claim 1, wherein the graphicalrepresentations comprise a trended process behavior chart, the trendedprocess behavior chart comprising: a trendline serving as a centerlineof the process behavior chart; parallel limit lines having the sameslope as and positioned to surround the trendline.
 7. The interfaceaccording to claim 1, further comprising a behavior chart interfacedisplayed in the first display area and configured to enable a user tosplit a process behavior chart into segments, the behavior chartcomprising an input box for receiving a beginning point of each segmentof the process behavior chart.
 8. The interface according to claim 1,wherein the graphical representations comprise: a chart graphicallyrepresenting statistical data; a data table containing the statisticaldata represented in the chart; wherein statistical data selected by auser in the chart is automatically highlighted in the data table.
 9. Theinterface according to claim 1, wherein statistical data selected by auser in one of the graphical representations is automaticallyhighlighted in other graphical representations.
 10. The interfaceaccording to claim 1, wherein the graphical representations comprise aprocess map configured to display an order of one or more process steps;wherein the order of the process steps is modifiable by a user.
 11. Theinterface according to claim 10, further comprising a step efficiencyvalue displayed in each process step.
 12. The interface according toclaim 1, further comprising one or more test variables for use in thequality analysis project steps and the statistical tools of the qualityanalysis project; wherein the statistical data and test variables usedin prior quality analysis project steps and statistical tools areautomatically carried forward for use in subsequent quality analysisproject steps and statistical tools.
 13. The interface according toclaim 1, wherein the graphical representations comprise a chartillustrating values of one or more variables ranked in order of theirpotential influence on a process; wherein one or more of the variablesis removable by a user.
 14. The interface according to claim 1, furthercomprising one or more user-defined input variables for use inperforming the quality analysis project steps; wherein one or more ofthe user-defined variables are designated by a user as an outputvariable, an interval input variable, or a categorical input variable.15. The interface according to claim 14, wherein a regression method isused to determine a relationship between the user-defined variables whenone or more user-defined variables are designated as interval inputvariables; and an analysis of variance (ANOVA) method is used todetermine a relationship between the user-defined variables when one ormore of the user-defined variables are designated as categorical inputvariables.
 16. The interface according to claim 1, wherein the graphicalrepresentations comprise a graphical gauge for graphically representinga power value of a statistical test.
 17. The interface according toclaim 16, wherein the graphical gauge is a “gas gauge” or a slider bar.18. The interface according to claim 1, further comprising a factorialexperiment design area displayed in the first display area, thefactorial experiment design area comprising: a first portion forreceiving an experiment type; a second portion for receiving a factornumber and a run number; a third portion for receiving an acceptablealpha risk; a fourth portion for receiving a replicate value, acenterpoint value, and a block value.
 19. The interface according toclaim 18, further comprising: a fifth portion for receiving aninteraction value; a sixth portion for receiving a P limit value. 20.The interface according to claim 1, wherein the graphicalrepresentations comprise a scree plot.
 21. The interface according toclaim 1, wherein the graphical representations comprise a fractionalfactorial display configured to graphically represent results of afractional factorial experiment; wherein statistically insignificantresults of the fractional factorial experiment are automaticallyhighlighted in the fractional factorial display.
 22. The interfaceaccording to claim 1, wherein the graphical representations comprise: afirst graph illustrating a distribution of a T-test when an actualdifference between two population samples is assumed to be less than apre-determined number; a second graph illustrating the distribution of aT-test when the actual difference between the two population samples isassumed to be more than the pre-determined number; a third graphillustrating the distribution of a T-test when the actual differencebetween the two population samples is assumed to be different from thepre-determined number.
 23. The interface according to claim 1, whereinthe graphical representations comprise: one or more data boxescontaining statistical data from a statistical test; explanatorystatement boxes containing text explaining the significance of thestatistical data contained in the data boxes.
 24. The interfaceaccording to claim 1, wherein the graphical representations comprise: afirst process behavior chart for graphically illustrating the value ofvariables in a single input variable process; a second process behaviorchart for graphically illustrating the value of residuals in a multipleinput variable process.
 25. The interface according to claim 1, whereinthe graphical representations comprise: a first input box for receivinga sample size value; a second input box for receiving a differencevalue; a third input box for receiving a power value; wherein one of thesample size value, the difference value, and the power value of the testis automatically computed and displayed after the other two values areprovided.
 26. The interface according to claim 1, wherein the graphicalrepresentations comprise a process behavior chart generated for auser-selected input variable.
 27. The interface according to claim 1,wherein the graphical representations comprise: a capability processbehavior chart; a capability histogram simultaneously displayed with theprocess behavior chart.
 28. The interface according to claim 1, whereinthe graphical representations comprise a chart, the chart comprising:user-defined upper and lower specification limits illustrating Supperand lower specification limits of a process; upper and lower uncertaintyzones configured to graphically display an amount of uncertainty presentin a measurement system relative to the width of the upper and lowerspecification limits.
 29. The interface according to claim 1, whereinthe graphical representations comprise a final project report, the finalproject report comprising one or more graphical summaries of each stepin the quality analysis project; wherein the final project report isautomatically generated upon completion of all of the quality analysisproject steps in the quality analysis project.
 30. The interfaceaccording to claim 1, wherein the graphical representations comprise asingle process behavior chart arranged into subgroups; wherein acenterline of each subgroup of the process behavior chart is centered ona subgroup mean.
 31. The interface according to claim 1, wherein thegraphical representations comprise a chart, the chart comprising: one ormore main variable columns graphically representing a magnitude of aneffect caused by a main input variable; one or more secondary variablecolumns blocked within each main variable column and graphicallyrepresenting a magnitude of an effect caused by a secondary inputvariable; a line graph displayed within each main variable column andgraphically representing a cumulative sum of the values of eachsecondary variable column.
 32. A system for displaying graphicalrepresentations of statistical data, comprising: processor; a displaydevice controlled by the processor to display; a first display area fordisplaying graphical representations of statistical data; a seconddisplay area for displaying; one or more quality analysis project stepsin a quality analysis project; one or more statistical tool categoriesassociated with the one or more quality analysis project steps; one ormore statistical tools associated with the one or more statistical toolcategories.
 33. A method for displaying graphical representations ofstatistical data, comprising: providing a first display area fordisplaying graphical representations of statistical data; providing asecond display area for displaying: one or more quality analysis projectsteps in a quality analysis project; one or more statistical toolcategories associated with the one or more quality analysis projectsteps; one or more statistical tools associated with the one or morestatistical tool categories.